






import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
   __A					=	"pt"
elif is_tf_available():
   __A					=	"tf"
else:
   __A					=	"jax"



class 				_A			(     UpperCamelCase					,					unittest.TestCase				):
    """simple docstring"""

    lowerCamelCase	:							List[str]				= ByTaTokenizer
    lowerCamelCase	:							Union[str, Any]				= False



    def _a							(				self      :     Dict      )      ->							List[str]:
           super().setUp()
           __UpperCAmelCase     				=ByTaTokenizer()
           tokenizer.save_pretrained(self.tmpdirname      )



    @cached_property
    def _a							(				self      :     int      )      ->							Optional[int]:
           return ByTaTokenizer.from_pretrained("""google/byt5-small"""      )



    def _a							(				self      :     Union[str, Any] ,     **__SCREAMING_SNAKE_CASE      :     Optional[Any]      )      ->							ByTaTokenizer:
           return self.tokenizer_class.from_pretrained(self.tmpdirname ,     **__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Dict ,     __SCREAMING_SNAKE_CASE      :     Tuple ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]=False ,     __SCREAMING_SNAKE_CASE      :     Optional[int]=20 ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]=5      )      ->							Tuple[str, list]:
           # XXX The default common tokenizer tests assume that every ID is decodable on its own.
           # This assumption is invalid for ByT5 because single bytes might not be
           # valid utf-8 (byte 128 for instance).
           # Here we're overriding the smallest possible method to provide
           # a clean sequence without making the same assumption.

           __UpperCAmelCase     				=[]
           for i in range(len(__SCREAMING_SNAKE_CASE      )      ):
                  try:
                         __UpperCAmelCase     				=tokenizer.decode([i] ,     clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE      )
                  except UnicodeDecodeError:
                         pass
                  toks.append((i, tok)      )

           __UpperCAmelCase     				=list(filter(lambda __SCREAMING_SNAKE_CASE      : re.match(R"""^[ a-zA-Z]+$""" ,     t[1]      ) ,     __SCREAMING_SNAKE_CASE      )      )
           __UpperCAmelCase     				=list(filter(lambda __SCREAMING_SNAKE_CASE      : [t[0]] == tokenizer.encode(t[1] ,     add_special_tokens=__SCREAMING_SNAKE_CASE      ) ,     __SCREAMING_SNAKE_CASE      )      )
           if max_length is not None and len(__SCREAMING_SNAKE_CASE      ) > max_length:
                  __UpperCAmelCase     				=toks[:max_length]
           if min_length is not None and len(__SCREAMING_SNAKE_CASE      ) < min_length and len(__SCREAMING_SNAKE_CASE      ) > 0:
                  while len(__SCREAMING_SNAKE_CASE      ) < min_length:
                         __UpperCAmelCase     				=toks + toks
        # toks_str = [t[1] for t in toks]
           __UpperCAmelCase     				=[t[0] for t in toks]

           # Ensure consistency
           __UpperCAmelCase     				=tokenizer.decode(__SCREAMING_SNAKE_CASE ,     clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE      )
           if " " not in output_txt and len(__SCREAMING_SNAKE_CASE      ) > 1:
                  __UpperCAmelCase     				=(
                      tokenizer.decode([toks_ids[0]] ,     clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE      )
                      + """ """
                      + tokenizer.decode(toks_ids[1:] ,     clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE      )
                  )
           if with_prefix_space:
                  __UpperCAmelCase     				=""" """ + output_txt
           __UpperCAmelCase     				=tokenizer.encode(__SCREAMING_SNAKE_CASE ,     add_special_tokens=__SCREAMING_SNAKE_CASE      )
           return output_txt, output_ids



    def _a							(				self      :     str      )      ->							Optional[int]:
           __UpperCAmelCase     				=self.ta_base_tokenizer
           __UpperCAmelCase     				=tokenizer(["""hi</s>""", """I went to the gym</s>""", """</s>"""]      )
           __UpperCAmelCase     				=tokenizer(["""hi""", """I went to the gym""", """"""]      )
           self.assertListEqual(batch_with_eos_added["""input_ids"""] ,     batch_without_eos_added["""input_ids"""]      )



    def _a							(				self      :     Union[str, Any]      )      ->							str:
           __UpperCAmelCase     				=self.ta_base_tokenizer
           __UpperCAmelCase     				="""Unicode €."""
           __UpperCAmelCase     				=tokenizer(__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=[88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
           self.assertEqual(encoded["""input_ids"""] ,     __SCREAMING_SNAKE_CASE      )

           # decoding
           __UpperCAmelCase     				=tokenizer.decode(__SCREAMING_SNAKE_CASE      )
           self.assertEqual(__SCREAMING_SNAKE_CASE ,     """Unicode €.</s>"""      )

           __UpperCAmelCase     				=tokenizer("""e è é ê ë"""      )
           __UpperCAmelCase     				=[104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
           self.assertEqual(encoded["""input_ids"""] ,     __SCREAMING_SNAKE_CASE      )
           # decoding
           __UpperCAmelCase     				=tokenizer.decode(__SCREAMING_SNAKE_CASE      )
           self.assertEqual(__SCREAMING_SNAKE_CASE ,     """e è é ê ë</s>"""      )

           # encode/decode, but with `encode` instead of `__call__`
           self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë"""      )      ) ,     """e è é ê ë</s>"""      )



    def _a							(				self      :     Any      )      ->							Union[str, Any]:
           __UpperCAmelCase     				=self.ta_base_tokenizer
           __UpperCAmelCase     				=["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
           # fmt: off
           __UpperCAmelCase     				=[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
           # fmt: on
           __UpperCAmelCase     				=tokenizer(__SCREAMING_SNAKE_CASE ,     padding=__SCREAMING_SNAKE_CASE ,     return_tensors=__SCREAMING_SNAKE_CASE      )
           self.assertIsInstance(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )

           if FRAMEWORK != "jax":
                  __UpperCAmelCase     				=list(batch.input_ids.numpy()[0]      )
           else:
                  __UpperCAmelCase     				=list(batch.input_ids.tolist()[0]      )

           self.assertListEqual(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )

           self.assertEqual((2, 37) ,     batch.input_ids.shape      )
           self.assertEqual((2, 37) ,     batch.attention_mask.shape      )



    def _a							(				self      :     int      )      ->							List[Any]:
           __UpperCAmelCase     				=self.ta_base_tokenizer
           __UpperCAmelCase     				=["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
           __UpperCAmelCase     				=tokenizer(__SCREAMING_SNAKE_CASE ,     padding=__SCREAMING_SNAKE_CASE ,     return_tensors=__SCREAMING_SNAKE_CASE      )
           # check if input_ids are returned and no decoder_input_ids
           self.assertIn("""input_ids""" ,     __SCREAMING_SNAKE_CASE      )
           self.assertIn("""attention_mask""" ,     __SCREAMING_SNAKE_CASE      )
           self.assertNotIn("""decoder_input_ids""" ,     __SCREAMING_SNAKE_CASE      )
           self.assertNotIn("""decoder_attention_mask""" ,     __SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Optional[Any]      )      ->							Optional[Any]:
           __UpperCAmelCase     				=self.ta_base_tokenizer
           __UpperCAmelCase     				=[
               """Summary of the text.""",
               """Another summary.""",
           ]
           __UpperCAmelCase     				=tokenizer(
               text_target=__SCREAMING_SNAKE_CASE ,     max_length=32 ,     padding="""max_length""" ,     truncation=__SCREAMING_SNAKE_CASE ,     return_tensors=__SCREAMING_SNAKE_CASE      )
           self.assertEqual(32 ,     targets["""input_ids"""].shape[1]      )



    def _a							(				self      :     int      )      ->							Tuple:
           __UpperCAmelCase     				=self.ta_base_tokenizer
           __UpperCAmelCase     				=["""A long paragraph for summarization. </s>"""]
           __UpperCAmelCase     				=["""Summary of the text. </s>"""]
           # fmt: off
           __UpperCAmelCase     				=[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
           __UpperCAmelCase     				=[86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
           # fmt: on

           __UpperCAmelCase     				=tokenizer(__SCREAMING_SNAKE_CASE ,     text_target=__SCREAMING_SNAKE_CASE      )

           self.assertEqual(__SCREAMING_SNAKE_CASE ,     batch["""input_ids"""][0]      )
           self.assertEqual(__SCREAMING_SNAKE_CASE ,     batch["""labels"""][0]      )



    def _a							(				self      :     Union[str, Any]      )      ->							List[str]:
           # safety check on max_len default value so we are sure the test works
           __UpperCAmelCase     				=self.get_tokenizers()
           for tokenizer in tokenizers:
                  with self.subTest(f'''{tokenizer.__class__.__name__}'''      ):
                         self.assertNotEqual(tokenizer.model_max_length ,     42      )

        # Now let's start the test
           __UpperCAmelCase     				=self.get_tokenizers()
           for tokenizer in tokenizers:
                  with self.subTest(f'''{tokenizer.__class__.__name__}'''      ):
                         # Isolate this from the other tests because we save additional tokens/etc
                         __UpperCAmelCase     				=tempfile.mkdtemp()

                         __UpperCAmelCase     				=""" He is very happy, UNwant\u00E9d,running"""
                         __UpperCAmelCase     				=tokenizer.encode(__SCREAMING_SNAKE_CASE ,     add_special_tokens=__SCREAMING_SNAKE_CASE      )
                         tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE      )

                         __UpperCAmelCase     				=tokenizer.__class__.from_pretrained(__SCREAMING_SNAKE_CASE      )
                         __UpperCAmelCase     				=after_tokenizer.encode(__SCREAMING_SNAKE_CASE ,     add_special_tokens=__SCREAMING_SNAKE_CASE      )
                         self.assertListEqual(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )

                         shutil.rmtree(__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=self.get_tokenizers(model_max_length=42      )
           for tokenizer in tokenizers:
                  with self.subTest(f'''{tokenizer.__class__.__name__}'''      ):
                         # Isolate this from the other tests because we save additional tokens/etc
                         __UpperCAmelCase     				=tempfile.mkdtemp()

                         __UpperCAmelCase     				=""" He is very happy, UNwant\u00E9d,running"""
                         tokenizer.add_tokens(["""bim""", """bambam"""]      )
                         __UpperCAmelCase     				=tokenizer.additional_special_tokens
                         additional_special_tokens.append("""new_additional_special_token"""      )
                         tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}      )
                         __UpperCAmelCase     				=tokenizer.encode(__SCREAMING_SNAKE_CASE ,     add_special_tokens=__SCREAMING_SNAKE_CASE      )
                         tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE      )

                         __UpperCAmelCase     				=tokenizer.__class__.from_pretrained(__SCREAMING_SNAKE_CASE      )
                         __UpperCAmelCase     				=after_tokenizer.encode(__SCREAMING_SNAKE_CASE ,     add_special_tokens=__SCREAMING_SNAKE_CASE      )
                         self.assertListEqual(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )
                         self.assertIn("""new_additional_special_token""" ,     after_tokenizer.additional_special_tokens      )
                         self.assertEqual(after_tokenizer.model_max_length ,     42      )

                         __UpperCAmelCase     				=tokenizer.__class__.from_pretrained(__SCREAMING_SNAKE_CASE ,     model_max_length=43      )
                         self.assertEqual(tokenizer.model_max_length ,     43      )

                         shutil.rmtree(__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     int      )      ->							Any:
           __UpperCAmelCase     				=[]
           if self.test_slow_tokenizer:
                  tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())      )

           if self.test_rust_tokenizer:
                  tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())      )

           for tokenizer_class, tokenizer_utils in tokenizer_list:
                  with tempfile.TemporaryDirectory() as tmp_dir:
                         tokenizer_utils.save_pretrained(__SCREAMING_SNAKE_CASE      )

                         with open(os.path.join(__SCREAMING_SNAKE_CASE ,     """special_tokens_map.json"""      ) ,     encoding="""utf-8"""      ) as json_file:
                                __UpperCAmelCase     				=json.load(__SCREAMING_SNAKE_CASE      )

                         with open(os.path.join(__SCREAMING_SNAKE_CASE ,     """tokenizer_config.json"""      ) ,     encoding="""utf-8"""      ) as json_file:
                                __UpperCAmelCase     				=json.load(__SCREAMING_SNAKE_CASE      )

                         __UpperCAmelCase     				=[f'''<extra_id_{i}>''' for i in range(125      )]

                         __UpperCAmelCase     				=added_tokens_extra_ids + [
                             """an_additional_special_token"""
                         ]
                         __UpperCAmelCase     				=added_tokens_extra_ids + [
                             """an_additional_special_token"""
                         ]

                         with open(os.path.join(__SCREAMING_SNAKE_CASE ,     """special_tokens_map.json"""      ) ,     """w""" ,     encoding="""utf-8"""      ) as outfile:
                                json.dump(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )
                         with open(os.path.join(__SCREAMING_SNAKE_CASE ,     """tokenizer_config.json"""      ) ,     """w""" ,     encoding="""utf-8"""      ) as outfile:
                                json.dump(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )

                         # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                         # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                         # "special_tokens_map.json" files
                         __UpperCAmelCase     				=tokenizer_class.from_pretrained(
                             __SCREAMING_SNAKE_CASE ,     )
                         self.assertIn(
                             """an_additional_special_token""" ,     tokenizer_without_change_in_init.additional_special_tokens      )
                         # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
                         self.assertEqual(
                             ["""an_additional_special_token"""] ,     tokenizer_without_change_in_init.convert_ids_to_tokens(
                                 tokenizer_without_change_in_init.convert_tokens_to_ids(["""an_additional_special_token"""]      )      ) ,     )

                         # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                         __UpperCAmelCase     				=added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token""" ,     lstrip=__SCREAMING_SNAKE_CASE      )]
                         __UpperCAmelCase     				=tokenizer_class.from_pretrained(
                             __SCREAMING_SNAKE_CASE ,     additional_special_tokens=__SCREAMING_SNAKE_CASE ,     )

                         self.assertIn("""a_new_additional_special_token""" ,     tokenizer.additional_special_tokens      )
                         self.assertEqual(
                             ["""a_new_additional_special_token"""] ,     tokenizer.convert_ids_to_tokens(
                                 tokenizer.convert_tokens_to_ids(["""a_new_additional_special_token"""]      )      ) ,     )



    def _a							(				self      :     str      )      ->							List[Any]:
           __UpperCAmelCase     				=[]
           if self.test_slow_tokenizer:
                  tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())      )

           if self.test_rust_tokenizer:
                  tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())      )

           for tokenizer_class, tokenizer_utils in tokenizer_list:
                  with tempfile.TemporaryDirectory() as tmp_dir:
                         tokenizer_utils.save_pretrained(__SCREAMING_SNAKE_CASE      )

                         __UpperCAmelCase     				=tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE      )

                         self.assertTrue(tokenizer.decode([255]      ) == """"""      )



    def _a							(				self      :     Union[str, Any]      )      ->							Optional[Any]:
           pass



    def _a							(				self      :     Optional[Any]      )      ->							List[Any]:
           pass



    def _a							(				self      :     Optional[Any]      )      ->							int:
           pass



    def _a							(				self      :     List[Any]      )      ->							int:
           pass



    def _a							(				self      :     Any      )      ->							Optional[int]:
           # The default common tokenizer tests uses invalid tokens for ByT5 that can only accept one-character strings
           # and special added tokens as tokens
           __UpperCAmelCase     				=self.get_tokenizers(fast=__SCREAMING_SNAKE_CASE ,     do_lower_case=__SCREAMING_SNAKE_CASE      )
           for tokenizer in tokenizers:
                  with self.subTest(f'''{tokenizer.__class__.__name__}'''      ):
                         __UpperCAmelCase     				=["""t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """x""", """t""", """</s>"""]
                         __UpperCAmelCase     				=tokenizer.convert_tokens_to_string(__SCREAMING_SNAKE_CASE      )

                         self.assertIsInstance(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     List[Any]      )      ->							Any:
           __UpperCAmelCase     				=self.get_tokenizers()
           for tokenizer in tokenizers:
                  with self.subTest(f'''{tokenizer.__class__.__name__}'''      ):
                         __UpperCAmelCase     				=[
                             """bos_token""",
                             """eos_token""",
                             """unk_token""",
                             """sep_token""",
                             """pad_token""",
                             """cls_token""",
                             """mask_token""",
                         ]

                         __UpperCAmelCase     				=0
                         __UpperCAmelCase     				=tokenizer.convert_ids_to_tokens(
                             __SCREAMING_SNAKE_CASE ,     skip_special_tokens=__SCREAMING_SNAKE_CASE      )

                         for attr in attributes_list:
                                setattr(__SCREAMING_SNAKE_CASE ,     attr + """_id""" ,     __SCREAMING_SNAKE_CASE      )
                                self.assertEqual(getattr(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      ) ,     __SCREAMING_SNAKE_CASE      )
                                self.assertEqual(getattr(__SCREAMING_SNAKE_CASE ,     attr + """_id"""      ) ,     __SCREAMING_SNAKE_CASE      )

                                setattr(__SCREAMING_SNAKE_CASE ,     attr + """_id""" ,     __SCREAMING_SNAKE_CASE      )
                                self.assertEqual(getattr(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      ) ,     __SCREAMING_SNAKE_CASE      )
                                self.assertEqual(getattr(__SCREAMING_SNAKE_CASE ,     attr + """_id"""      ) ,     __SCREAMING_SNAKE_CASE      )

                         setattr(__SCREAMING_SNAKE_CASE ,     """additional_special_tokens_ids""" ,     []      )
                         self.assertListEqual(getattr(__SCREAMING_SNAKE_CASE ,     """additional_special_tokens"""      ) ,     []      )
                         self.assertListEqual(getattr(__SCREAMING_SNAKE_CASE ,     """additional_special_tokens_ids"""      ) ,     []      )

                         setattr(__SCREAMING_SNAKE_CASE ,     """additional_special_tokens_ids""" ,     [token_id_to_test_setters]      )
                         self.assertListEqual(getattr(__SCREAMING_SNAKE_CASE ,     """additional_special_tokens"""      ) ,     [token_to_test_setters]      )
                         self.assertListEqual(getattr(__SCREAMING_SNAKE_CASE ,     """additional_special_tokens_ids"""      ) ,     [token_id_to_test_setters]      )










'''simple docstring'''







import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=True      ,UpperCAmelCase="pt"						):






   '''simple docstring'''







   A__       =     {'add_prefix_space': True} if isinstance(UpperCAmelCase      ,UpperCAmelCase						) and not line.startswith(' '						) else {}
   A__       =     padding_side
   return tokenizer(
       [line]      ,max_length=UpperCAmelCase      ,padding='max_length' if pad_to_max_length else None      ,truncation=UpperCAmelCase      ,return_tensors=UpperCAmelCase      ,add_special_tokens=UpperCAmelCase      ,**UpperCAmelCase      ,)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=None      ,):






   '''simple docstring'''







   A__       =     input_ids.ne(UpperCAmelCase						).any(dim=0						)
   if attention_mask is None:
      return input_ids[:, keep_column_mask]
   else:
      return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])

class 	_snake_case(			UpperCAmelCase      ):






 def __init__(self       :  List[str]    ,		a       :  Any    ,		a       :  Union[str, Any]    ,		a       :  int    ,		a       :  Optional[int]    ,		a       :  Optional[Any]="train"    ,		a       :  Optional[int]=None    ,		a       :  Tuple=None    ,		a       :  str=None    ,		a       :  Dict=""    ,		)       ->   Dict:






    """simple docstring"""


    super().__init__()
    A__       =     Path(a    ).joinpath(type_path + '.source'    )
    A__       =     Path(a    ).joinpath(type_path + '.target'    )
    A__       =     self.get_char_lens(self.src_file    )
    A__       =     max_source_length
    A__       =     max_target_length
    assert min(self.src_lens    ) > 0, f"""found empty line in {self.src_file}"""
    A__       =     tokenizer
    A__       =     prefix
    if n_obs is not None:
       A__       =     self.src_lens[:n_obs]
    A__       =     src_lang
    A__       =     tgt_lang






 def __len__(self       :  Optional[Any]    )       ->   List[str]:






    """simple docstring"""


    return len(self.src_lens    )






 def __getitem__(self       :  Any    ,		a       :  str    )       ->   Dict[str, torch.Tensor]:






    """simple docstring"""


    A__       =     index + 1  # linecache starts at 1
    A__       =     self.prefix + linecache.getline(str(self.src_file    )    ,		a    ).rstrip('\n'    )
    A__       =     linecache.getline(str(self.tgt_file    )    ,		a    ).rstrip('\n'    )
    assert source_line, f"""empty source line for index {index}"""
    assert tgt_line, f"""empty tgt line for index {index}"""

    # Need to add eos token manually for T5
    if isinstance(self.tokenizer    ,		a    ):
       source_line += self.tokenizer.eos_token
       tgt_line += self.tokenizer.eos_token

    # Pad source and target to the right
    A__       =     (
        self.tokenizer.question_encoder if isinstance(self.tokenizer    ,		a    ) else self.tokenizer
    )
    A__       =     self.tokenizer.generator if isinstance(self.tokenizer    ,		a    ) else self.tokenizer

    A__       =     encode_line(a    ,		a    ,		self.max_source_length    ,		'right'    )
    A__       =     encode_line(a    ,		a    ,		self.max_target_length    ,		'right'    )

    A__       =     source_inputs['input_ids'].squeeze()
    A__       =     target_inputs['input_ids'].squeeze()
    A__       =     source_inputs['attention_mask'].squeeze()
    return {
        "input_ids": source_ids,
        "attention_mask": src_mask,
        "decoder_input_ids": target_ids,
    }






 @staticmethod
 def 						_UpperCamelCase (a       :  Tuple    )       ->   Tuple:






    """simple docstring"""


    return [len(a    ) for x in Path(a    ).open().readlines()]






 def 						_UpperCamelCase (self       :  str    ,		a       :  int    )       ->   Dict[str, torch.Tensor]:






    """simple docstring"""


    A__       =     torch.stack([x['input_ids'] for x in batch]    )
    A__       =     torch.stack([x['attention_mask'] for x in batch]    )
    A__       =     torch.stack([x['decoder_input_ids'] for x in batch]    )
    A__       =     (
        self.tokenizer.generator.pad_token_id
        if isinstance(self.tokenizer    ,		a    )
        else self.tokenizer.pad_token_id
    )
    A__       =     (
        self.tokenizer.question_encoder.pad_token_id
        if isinstance(self.tokenizer    ,		a    )
        else self.tokenizer.pad_token_id
    )
    A__       =     trim_batch(a    ,		a    )
    A__  ,			A__       =     trim_batch(a    ,		a    ,		attention_mask=a    )
    A__       =     {
        'input_ids': source_ids,
        'attention_mask': source_mask,
        'decoder_input_ids': y,
    }
    return batch




lowerCAmelCase_     						=				getLogger(__name__)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   return list(itertools.chain.from_iterable(UpperCAmelCase						)						)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     get_git_info()
   save_json(UpperCAmelCase      ,os.path.join(UpperCAmelCase      ,'git_log.json'						)						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=4      ,**UpperCAmelCase						):






   '''simple docstring'''







   with open(UpperCAmelCase      ,'w'						) as f:
      json.dump(UpperCAmelCase      ,UpperCAmelCase      ,indent=UpperCAmelCase      ,**UpperCAmelCase						)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   with open(UpperCAmelCase						) as f:
      return json.load(UpperCAmelCase						)







def 						_A      ( ):






   '''simple docstring'''







   A__       =     git.Repo(search_parent_directories=UpperCAmelCase						)
   A__       =     {
       'repo_id': str(UpperCAmelCase						),
       'repo_sha': str(repo.head.object.hexsha						),
       'repo_branch': str(repo.active_branch						),
       'hostname': str(socket.gethostname()						),
   }
   return repo_infos







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   return list(map(UpperCAmelCase      ,UpperCAmelCase						)						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   with open(UpperCAmelCase      ,'wb'						) as f:
      return pickle.dump(UpperCAmelCase      ,UpperCAmelCase						)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   def remove_articles(UpperCAmelCase						):
      return re.sub(r'\b(a|an|the)\b'      ,' '      ,UpperCAmelCase						)

   def white_space_fix(UpperCAmelCase						):
      return " ".join(text.split()						)

   def remove_punc(UpperCAmelCase						):
      A__       =     set(string.punctuation						)
      return "".join(ch for ch in text if ch not in exclude						)

   def lower(UpperCAmelCase						):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase						)						)						)						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     normalize_answer(UpperCAmelCase						).split()
   A__       =     normalize_answer(UpperCAmelCase						).split()
   A__       =     Counter(UpperCAmelCase						) & Counter(UpperCAmelCase						)
   A__       =     sum(common.values()						)
   if num_same == 0:
      return 0
   A__       =     1.0 * num_same / len(UpperCAmelCase						)
   A__       =     1.0 * num_same / len(UpperCAmelCase						)
   A__       =     (2 * precision * recall) / (precision + recall)
   return fa







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   return normalize_answer(UpperCAmelCase						) == normalize_answer(UpperCAmelCase						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   assert len(UpperCAmelCase						) == len(UpperCAmelCase						)
   A__       =     0
   for hypo, pred in zip(UpperCAmelCase      ,UpperCAmelCase						):
      em += exact_match_score(UpperCAmelCase      ,UpperCAmelCase						)
   if len(UpperCAmelCase						) > 0:
      em /= len(UpperCAmelCase						)
   return {"em": em}







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   return model_prefix.startswith('rag'						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     {p: p for p in extra_params}
   # T5 models don't have `dropout` param, they have `dropout_rate` instead
   A__       =     'dropout_rate'
   for p in extra_params:
      if getattr(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):
         if not hasattr(UpperCAmelCase      ,UpperCAmelCase						) and not hasattr(UpperCAmelCase      ,equivalent_param[p]						):
            logger.info('config doesn\'t have a `{}` attribute'.format(UpperCAmelCase						)						)
            delattr(UpperCAmelCase      ,UpperCAmelCase						)
            continue
         A__       =     p if hasattr(UpperCAmelCase      ,UpperCAmelCase						) else equivalent_param[p]
         setattr(UpperCAmelCase      ,UpperCAmelCase      ,getattr(UpperCAmelCase      ,UpperCAmelCase						)						)
         delattr(UpperCAmelCase      ,UpperCAmelCase						)
   return hparams, config


